3,924 research outputs found
Autoencoding a Soft Touch to Learn Grasping from On-land to Underwater
Robots play a critical role as the physical agent of human operators in
exploring the ocean. However, it remains challenging to grasp objects reliably
while fully submerging under a highly pressurized aquatic environment with
little visible light, mainly due to the fluidic interference on the tactile
mechanics between the finger and object surfaces. This study investigates the
transferability of grasping knowledge from on-land to underwater via a
vision-based soft robotic finger that learns 6D forces and torques (FT) using a
Supervised Variational Autoencoder (SVAE). A high-framerate camera captures the
whole-body deformations while a soft robotic finger interacts with physical
objects on-land and underwater. Results show that the trained SVAE model
learned a series of latent representations of the soft mechanics transferrable
from land to water, presenting a superior adaptation to the changing
environments against commercial FT sensors. Soft, delicate, and reactive
grasping enabled by tactile intelligence enhances the gripper's underwater
interaction with improved reliability and robustness at a much-reduced cost,
paving the path for learning-based intelligent grasping to support fundamental
scientific discoveries in environmental and ocean research.Comment: 17 pages, 5 figures, 1 table, submitted to Advanced Intelligent
Systems for revie
Proprioceptive Learning with Soft Polyhedral Networks
Proprioception is the "sixth sense" that detects limb postures with motor
neurons. It requires a natural integration between the musculoskeletal systems
and sensory receptors, which is challenging among modern robots that aim for
lightweight, adaptive, and sensitive designs at a low cost. Here, we present
the Soft Polyhedral Network with an embedded vision for physical interactions,
capable of adaptive kinesthesia and viscoelastic proprioception by learning
kinetic features. This design enables passive adaptations to omni-directional
interactions, visually captured by a miniature high-speed motion tracking
system embedded inside for proprioceptive learning. The results show that the
soft network can infer real-time 6D forces and torques with accuracies of
0.25/0.24/0.35 N and 0.025/0.034/0.006 Nm in dynamic interactions. We also
incorporate viscoelasticity in proprioception during static adaptation by
adding a creep and relaxation modifier to refine the predicted results. The
proposed soft network combines simplicity in design, omni-adaptation, and
proprioceptive sensing with high accuracy, making it a versatile solution for
robotics at a low cost with more than 1 million use cycles for tasks such as
sensitive and competitive grasping, and touch-based geometry reconstruction.
This study offers new insights into vision-based proprioception for soft robots
in adaptive grasping, soft manipulation, and human-robot interaction.Comment: 20 pages, 10 figures, 2 tables, submitted to the International
Journal of Robotics Research for revie
ChainQueen: A Real-Time Differentiable Physical Simulator for Soft Robotics
Physical simulators have been widely used in robot planning and control.
Among them, differentiable simulators are particularly favored, as they can be
incorporated into gradient-based optimization algorithms that are efficient in
solving inverse problems such as optimal control and motion planning.
Simulating deformable objects is, however, more challenging compared to rigid
body dynamics. The underlying physical laws of deformable objects are more
complex, and the resulting systems have orders of magnitude more degrees of
freedom and therefore they are significantly more computationally expensive to
simulate. Computing gradients with respect to physical design or controller
parameters is typically even more computationally challenging. In this paper,
we propose a real-time, differentiable hybrid Lagrangian-Eulerian physical
simulator for deformable objects, ChainQueen, based on the Moving Least Squares
Material Point Method (MLS-MPM). MLS-MPM can simulate deformable objects
including contact and can be seamlessly incorporated into inference, control
and co-design systems. We demonstrate that our simulator achieves high
precision in both forward simulation and backward gradient computation. We have
successfully employed it in a diverse set of control tasks for soft robots,
including problems with nearly 3,000 decision variables.Comment: In submission to ICRA 2019. Supplemental Video:
https://www.youtube.com/watch?v=4IWD4iGIsB4 Project Page:
https://github.com/yuanming-hu/ChainQuee
Defo-Net: Learning Body Deformation using Generative Adversarial Networks
Modelling the physical properties of everyday objects is a fundamental
prerequisite for autonomous robots. We present a novel generative adversarial
network (Defo-Net), able to predict body deformations under external forces
from a single RGB-D image. The network is based on an invertible conditional
Generative Adversarial Network (IcGAN) and is trained on a collection of
different objects of interest generated by a physical finite element model
simulator. Defo-Net inherits the generalisation properties of GANs. This means
that the network is able to reconstruct the whole 3-D appearance of the object
given a single depth view of the object and to generalise to unseen object
configurations. Contrary to traditional finite element methods, our approach is
fast enough to be used in real-time applications. We apply the network to the
problem of safe and fast navigation of mobile robots carrying payloads over
different obstacles and floor materials. Experimental results in real scenarios
show how a robot equipped with an RGB-D camera can use the network to predict
terrain deformations under different payload configurations and use this to
avoid unsafe areas.Comment: In ICRA 201
Unsupervised Sim-to-Real Adaptation of Soft Robot Proprioception using a Dual Cross-modal Autoencoder
Soft robotics is a modern robotic paradigm for performing dexterous
interactions with the surroundings via morphological flexibility. The desire
for autonomous operation requires soft robots to be capable of proprioception
and makes it necessary to devise a calibration process. These requirements can
be greatly benefited by adopting numerical simulation for computational
efficiency. However, the gap between the simulated and real domains limits the
accurate, generalized application of the approach. Herein, we propose an
unsupervised domain adaptation framework as a data-efficient, generalized
alignment of these heterogeneous sensor domains. A dual cross-modal autoencoder
was designed to match the sensor domains at a feature level without any
extensive labeling process, facilitating the computationally efficient
transferability to various tasks. As a proof-of-concept, the methodology was
adopted to the famous soft robot design, a multigait soft robot, and two
fundamental perception tasks for autonomous robot operation, involving
high-fidelity shape estimation and collision detection. The resulting
perception demonstrates the digital-twinned calibration process in both the
simulated and real domains. The proposed design outperforms the existing
prevalent benchmarks for both perception tasks. This unsupervised framework
envisions a new approach to imparting embodied intelligence to soft robotic
systems via blending simulation.Comment: 13 pages, 12 figure
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